Google’s Antitrust Woes and Google Shopping
Bibliographic record
Abstract
In 2017, Google was fined 2.7 billion USD by the European Commission for its abuse of dominance when it promoted its Google Shopping service above rival comparison shopping services on Google’s results page. Comparison shopping services suffered from the conduct as they received less traffic as users selected Google’s prominently placed Google Shopping service. This thesis will question whether Google’s conduct was anticompetitive, or were they incorrectly fined for pro-competitive conduct? Additionally, Canada’s Competition Bureau and the United States’ Federal Trade Commission exonerated Google for the same activity in past years, so why would these agencies with similar goals and legislation come to different opinions regarding Google’s conduct? This thesis concludes that Google’s conduct is pro-competitive and that the United States and Canada were correct in their decision to cease the investigation. This thesis also identifies three reasons why the European Commission could have legally justified fining Google, even if it was not economically justified. The analysis conducted in this thesis could give guidance to other similar cases which Google is being investigated for, such as the investigation into Google Flights and Google Maps.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".